setwd('~/Desktop/PLS397/')
#Looking to find what drives car sales in the US,
#and to see if we can predict car sales in the coming years after the current recession
#-#--------------------------------------------------------------------------#-#
#read in csv data
car_sales = read_csv("ALTSALES.csv")
## Parsed with column specification:
## cols(
## DATE = col_date(format = ""),
## ALTSALES = col_double()
## )
#clean up data
car_sales <- mutate(car_sales, Sales = ALTSALES )
car_sales <- subset(car_sales, select = -ALTSALES )
car_sales <- mutate(car_sales, Date = DATE )
car_sales <- subset(car_sales, select = -DATE )
car_sales <- na.omit(car_sales)
library(plotly)
## Warning: package 'plotly' was built under R version 3.6.2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggmap':
##
## wind
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(shiny)
#inteactive plot of car sales over time in the US
#car_plot = plot_ly(car_sales, x = ~Date, y = ~Sales, type= 'scatter', mode='lines')
car_plot <- plot_ly() %>%
add_trace(data = car_sales,
x = ~Date,
y = ~Sales,
colors = "BrBG",
type = "scatter",
mode = "line") %>%
layout(title = "Car Sales x Time",
xaxis = list(title = "Date"))
htmlwidgets::saveWidget(car_plot, "CarSales.html")
car_plot
getwd()
## [1] "/Users/carsondobozy/Desktop"
setwd("~/Desktop/PLS397")
#Unemployment Numbers CSV data
#chose unemployment becuase that seems to be one of the biggest deciding factors of car sales
#after doing research
Unemployment = read_csv("CCSA.csv")
## Parsed with column specification:
## cols(
## DATE = col_date(format = ""),
## CCSA = col_character()
## )
Unemployment <- mutate(Unemployment, Unemployed = CCSA )
Unemployment <- subset(Unemployment, select = -CCSA )
Unemployment <- mutate(Unemployment, Date = DATE )
Unemployment <- subset(Unemployment, select = -DATE )
Unemployment <- na.omit(Unemployment)
Unemployment$Unemployed = as.numeric(as.character(Unemployment$Unemployed))
## Warning: NAs introduced by coercion
#Make sure data is numeric
is.num <- sapply(Unemployment, is.numeric)
Unemployment[is.num] <- lapply(Unemployment[is.num], round, 8)
Unemployment
## # A tibble: 640 x 2
## Unemployed Date
## <dbl> <date>
## 1 1118750 1967-01-01
## 2 1162500 1967-02-01
## 3 1243250 1967-03-01
## 4 1281000 1967-04-01
## 5 1277500 1967-05-01
## 6 1246000 1967-06-01
## 7 1246200 1967-07-01
## 8 1209250 1967-08-01
## 9 1181400 1967-09-01
## 10 1172250 1967-10-01
## # … with 630 more rows
#plot to show unemployment over time
Unemp_plot <- plot_ly() %>%
add_trace(data = Unemployment,
x = ~Date,
y = ~Unemployed,
colors = "red",
type = "scatter",
mode = "line") %>%
layout(title = "People on Unemployment x Time",
xaxis = list(title = "Date"))
Unemp_plot
## Warning: Ignoring 1 observations
set.seed(420)
#train and test data for car sales
split1=caTools::sample.split(car_sales$Sales,SplitRatio = 0.8)
training_set1=subset(car_sales,split1==TRUE)
test_set1=subset(car_sales,split1==FALSE)
#Fitting regression model to the training set with unemployment levels
regressor1=lm(formula=car_sales$Sales[0:531]~Unemployment$Unemployed[109:639],
data=training_set1)
#Predicting the test set
y_pred1=predict(regressor1,newdata=test_set1)
## Warning: 'newdata' had 107 rows but variables found have 531 rows
#predicted car sales model (unemployment regression)
new_date1 = car_sales$Date[0:531]
car_pred1 = plot_ly(car_sales, x = new_date1, y = y_pred1, type= 'scatter', mode='lines', color='red')
car_pred1
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
#subplot to show Original Data vs model
#here is our first predicted time series with just Unemp an predictor
subplot(car_plot, car_pred1, nrows = 2, margin = 0.05, shareX = TRUE)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
getwd()
## [1] "/Users/carsondobozy/Desktop"
setwd("~/Desktop/PLS397")
#Consumption levels CSV data
#Consumption is obviously related to car sales, and has good response to major issues that cause market disruption
#i.e 9/11 attacks, 2008 market crash, corona virus pandemic
Consumption = read_csv("PCEDG.csv")
## Parsed with column specification:
## cols(
## DATE = col_date(format = ""),
## PCEDG = col_double()
## )
Consumption <- mutate(Consumption, Personal_Consumption = PCEDG )
Consumption <- subset(Consumption, select = -PCEDG )
Consumption <- mutate(Consumption, Date = DATE )
Consumption <- subset(Consumption, select = -DATE )
Consumption <- na.omit(Consumption)
#make sure data is numeric
Consumption <- rbind(Consumption, c(1124.0, "2020-03-01"))
Consumption <- transform(Consumption, Personal_Consumption = as.numeric(Personal_Consumption))
head(Consumption)
## Personal_Consumption Date
## 1 42.3 1959-01-01
## 2 44.2 1959-02-01
## 3 44.4 1959-03-01
## 4 45.1 1959-04-01
## 5 45.4 1959-05-01
## 6 46.0 1959-06-01
set.seed(420)
#train and test data for car sales
split2=caTools::sample.split(car_sales$Sales,SplitRatio = 0.8)
training_set2=subset(car_sales,split2==TRUE)
test_set2=subset(car_sales,split2==FALSE)
#Fitting regression model to the training set Unemployment and Consumption
regressor2=lm(formula=car_sales$Sales[0:531]~Unemployment$Unemployed[109:639] + Consumption$Personal_Consumption[205:735],
data=training_set2)
#Predicting the test set
y_pred2=predict(regressor2,newdata=test_set2)
## Warning: 'newdata' had 107 rows but variables found have 531 rows
#prediction model 2 (accounts for more variation)
new_date2 = car_sales$Date[0:531]
car_pred2 = plot_ly(car_sales, x = new_date2, y = y_pred2, type= 'scatter', mode='lines', color='red')
car_pred2
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
#subplot to show our two models vs actual data
#here we can see our model is becoming more accurate and more sensetive to volatility
subplot(car_plot, car_pred1, car_pred2, nrows = 3, margin = 0.05, shareX = TRUE)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
getwd()
## [1] "/Users/carsondobozy/Desktop"
setwd("~/Desktop/PLS397")
#Gas prices csv data
#Gas prices will give our model a better sensitivity and feel for how car sales move over time
GasPrices = read_csv("APU000074714.csv")
## Parsed with column specification:
## cols(
## DATE = col_date(format = ""),
## APU000074714 = col_double()
## )
GasPrices <- mutate(GasPrices, Gas_Price = APU000074714 )
GasPrices <- subset(GasPrices, select = -APU000074714 )
GasPrices <- mutate(GasPrices, Date = DATE )
GasPrices <- subset(GasPrices, select = -DATE )
GasPrices <- na.omit(GasPrices)
#create dataframe with all variables
data = data.frame(Date = new_date1[0:531],
CarSales = car_sales$Sales[0:531],
Unemployed = Unemployment$Unemployed[109:639],
Consumption = Consumption$Personal_Consumption[205:735],
GasPrice = GasPrices$Gas_Price[0:531]
)
data
## Date CarSales Unemployed Consumption GasPrice
## 1 1976-01-01 12.512 3035200 161.2 0.605
## 2 1976-02-01 13.044 2865750 164.2 0.600
## 3 1976-03-01 13.085 2843000 164.5 0.594
## 4 1976-04-01 12.915 2872250 167.6 0.592
## 5 1976-05-01 12.645 2941000 162.2 0.600
## 6 1976-06-01 12.727 2989500 167.9 0.616
## 7 1976-07-01 13.122 2995800 169.7 0.623
## 8 1976-08-01 12.594 3015250 168.7 0.628
## 9 1976-09-01 13.209 3070000 171.2 0.630
## 10 1976-10-01 12.537 3093000 170.9 0.629
## 11 1976-11-01 13.006 3055750 173.2 0.629
## 12 1976-12-01 14.197 2923500 182.2 0.626
## 13 1977-01-01 14.019 2827200 177.8 0.627
## 14 1977-02-01 14.335 2849000 184.1 0.637
## 15 1977-03-01 14.782 2702750 189.9 0.643
## 16 1977-04-01 14.753 2671200 188.9 0.651
## 17 1977-05-01 14.605 2631250 188.6 0.659
## 18 1977-06-01 14.571 2619250 190.2 0.665
## 19 1977-07-01 14.306 2583000 191.7 0.667
## 20 1977-08-01 14.488 2610500 194.2 0.667
## 21 1977-09-01 14.274 2589500 196.1 0.666
## 22 1977-10-01 14.511 2576200 197.1 0.665
## 23 1977-11-01 14.396 2568500 200.8 0.664
## 24 1977-12-01 14.789 2507000 204.2 0.665
## 25 1978-01-01 13.334 2493500 191.2 0.648
## 26 1978-02-01 13.969 2555500 198.5 0.647
## 27 1978-03-01 14.714 2499750 204.7 0.647
## 28 1978-04-01 15.757 2349600 213.5 0.649
## 29 1978-05-01 15.830 2231500 217.0 0.655
## 30 1978-06-01 15.868 2221750 218.1 0.663
## 31 1978-07-01 15.086 2313200 215.3 0.674
## 32 1978-08-01 15.676 2398000 221.8 0.682
## 33 1978-09-01 14.195 2269800 213.1 0.688
## 34 1978-10-01 15.391 2230500 220.3 0.690
## 35 1978-11-01 15.015 2223750 222.3 0.695
## 36 1978-12-01 14.839 2286600 224.2 0.705
## 37 1979-01-01 14.626 2382500 220.5 0.716
## 38 1979-02-01 14.856 2414250 224.0 0.730
## 39 1979-03-01 14.839 2385800 225.3 0.755
## 40 1979-04-01 14.304 2398500 222.7 0.802
## 41 1979-05-01 13.966 2259250 224.4 0.844
## 42 1979-06-01 12.363 2251600 219.3 0.901
## 43 1979-07-01 13.712 2358500 226.0 0.949
## 44 1979-08-01 13.999 2458250 231.3 0.988
## 45 1979-09-01 13.953 2428200 235.4 1.020
## 46 1979-10-01 12.733 2490000 228.3 1.028
## 47 1979-11-01 12.667 2602500 229.0 1.041
## 48 1979-12-01 13.198 2698800 229.9 1.065
## 49 1980-01-01 14.115 2781750 239.6 1.131
## 50 1980-02-01 12.967 2868500 232.9 1.207
## 51 1980-03-01 11.797 2881200 223.6 1.252
## 52 1980-04-01 10.198 3126500 212.4 1.264
## 53 1980-05-01 9.310 3544800 208.5 1.266
## 54 1980-06-01 10.181 3832500 214.9 1.269
## 55 1980-07-01 11.381 3845500 226.7 1.271
## 56 1980-08-01 10.860 3805200 224.0 1.267
## 57 1980-09-01 10.622 3693500 225.4 1.257
## 58 1980-10-01 11.146 3556250 236.3 1.250
## 59 1980-11-01 11.014 3300600 236.7 1.250
## 60 1980-12-01 10.718 3125500 235.7 1.258
## 61 1981-01-01 11.036 3038200 239.9 1.298
## 62 1981-02-01 12.116 2978000 247.3 1.382
## 63 1981-03-01 12.185 2899250 250.9 1.417
## 64 1981-04-01 10.212 2857000 241.7 1.412
## 65 1981-05-01 10.136 2904000 239.8 1.400
## 66 1981-06-01 10.205 2903250 241.4 1.391
## 67 1981-07-01 10.206 2883000 245.2 1.382
## 68 1981-08-01 12.457 2948400 260.4 1.376
## 69 1981-09-01 10.863 3013000 250.2 1.376
## 70 1981-10-01 9.209 3127000 237.3 1.371
## 71 1981-11-01 9.294 3337250 237.0 1.369
## 72 1981-12-01 8.849 3525250 236.3 1.365
## 73 1982-01-01 10.075 3533600 241.0 1.358
## 74 1982-02-01 10.725 3555500 249.3 1.334
## 75 1982-03-01 10.457 3655500 249.0 1.284
## 76 1982-04-01 9.541 3864500 244.9 1.225
## 77 1982-05-01 10.900 3991800 256.8 1.237
## 78 1982-06-01 9.420 4094250 246.0 1.309
## 79 1982-07-01 9.585 4101400 247.8 1.331
## 80 1982-08-01 9.779 4260250 248.6 1.323
## 81 1982-09-01 10.958 4452750 260.0 1.307
## 82 1982-10-01 9.981 4639600 254.1 1.295
## 83 1982-11-01 11.866 4601000 268.1 1.283
## 84 1982-12-01 11.073 4400000 270.8 1.260
## 85 1983-01-01 10.688 3919000 268.3 1.230
## 86 1983-02-01 10.441 3859250 266.8 1.187
## 87 1983-03-01 10.832 3767500 271.6 1.152
## 88 1983-04-01 11.507 3760200 281.7 1.215
## 89 1983-05-01 11.865 3624250 288.8 1.259
## 90 1983-06-01 12.785 3455500 298.2 1.277
## 91 1983-07-01 12.733 3189600 303.4 1.288
## 92 1983-08-01 12.048 3105750 301.2 1.285
## 93 1983-09-01 12.258 3007000 302.6 1.274
## 94 1983-10-01 13.103 2902800 313.7 1.255
## 95 1983-11-01 12.899 2787750 315.9 1.241
## 96 1983-12-01 14.278 2712200 328.3 1.231
## 97 1984-01-01 13.998 2549250 336.8 1.216
## 98 1984-02-01 14.130 2469500 328.7 1.209
## 99 1984-03-01 13.967 2424400 328.8 1.210
## 100 1984-04-01 14.094 2422250 336.0 1.227
## 101 1984-05-01 14.569 2385000 341.7 1.236
## 102 1984-06-01 14.394 2360400 346.1 1.229
## 103 1984-07-01 14.455 2353000 341.5 1.212
## 104 1984-08-01 14.006 2385750 339.7 1.196
## 105 1984-09-01 13.662 2434800 345.0 1.203
## 106 1984-10-01 14.342 2524000 343.0 1.209
## 107 1984-11-01 14.444 2584250 358.4 1.207
## 108 1984-12-01 14.306 2547000 360.1 1.193
## 109 1985-01-01 15.265 2552250 365.5 1.148
## 110 1985-02-01 15.432 2623500 364.9 1.131
## 111 1985-03-01 15.049 2564400 373.6 1.159
## 112 1985-04-01 15.224 2578750 365.7 1.205
## 113 1985-05-01 15.270 2560750 384.3 1.231
## 114 1985-06-01 14.800 2551800 369.7 1.241
## 115 1985-07-01 15.222 2542500 376.3 1.242
## 116 1985-08-01 16.633 2562600 390.3 1.229
## 117 1985-09-01 18.809 2580500 422.7 1.216
## 118 1985-10-01 13.887 2631750 379.2 1.204
## 119 1985-11-01 14.333 2625200 380.8 1.207
## 120 1985-12-01 15.387 2643750 391.2 1.208
## 121 1986-01-01 15.781 2604000 401.4 1.194
## 122 1986-02-01 15.126 2568500 389.4 1.120
## 123 1986-03-01 13.900 2593600 384.1 0.981
## 124 1986-04-01 15.579 2608750 404.0 0.888
## 125 1986-05-01 16.038 2671000 414.2 0.923
## 126 1986-06-01 15.637 2635000 403.7 0.955
## 127 1986-07-01 15.471 2608000 415.9 0.890
## 128 1986-08-01 17.010 2655600 434.4 0.843
## 129 1986-09-01 21.221 2707000 486.8 0.860
## 130 1986-10-01 14.516 2718750 434.9 0.831
## 131 1986-11-01 14.545 2630800 420.0 0.821
## 132 1986-12-01 17.782 2571250 468.5 0.823
## 133 1987-01-01 12.155 2512400 399.7 0.862
## 134 1987-02-01 14.830 2472000 427.0 0.905
## 135 1987-03-01 15.017 2429250 428.8 0.912
## 136 1987-04-01 15.167 2411000 437.6 0.934
## 137 1987-05-01 14.283 2324000 434.0 0.941
## 138 1987-06-01 15.341 2279000 445.7 0.958
## 139 1987-07-01 15.372 2196500 452.4 0.971
## 140 1987-08-01 16.786 2211600 468.9 0.995
## 141 1987-09-01 15.820 2196750 460.3 0.990
## 142 1987-10-01 13.975 2046800 443.1 0.976
## 143 1987-11-01 14.227 2082250 447.4 0.976
## 144 1987-12-01 15.354 2115500 459.4 0.961
## 145 1988-01-01 15.770 2188000 470.3 0.933
## 146 1988-02-01 16.172 2141500 468.7 0.913
## 147 1988-03-01 15.848 2112000 472.4 0.904
## 148 1988-04-01 15.201 2081200 467.8 0.930
## 149 1988-05-01 15.606 2067750 474.7 0.955
## 150 1988-06-01 15.525 2043000 477.3 0.955
## 151 1988-07-01 15.375 2065200 472.4 0.967
## 152 1988-08-01 15.078 2088000 470.1 0.987
## 153 1988-09-01 14.710 2075750 468.7 0.974
## 154 1988-10-01 14.815 2015600 477.9 0.957
## 155 1988-11-01 15.038 1978250 481.8 0.949
## 156 1988-12-01 16.098 2045200 498.9 0.930
## 157 1989-01-01 15.017 2061000 496.2 0.918
## 158 1989-02-01 14.668 2113750 481.3 0.926
## 159 1989-03-01 14.362 2131500 481.9 0.940
## 160 1989-04-01 15.698 2098800 500.4 1.065
## 161 1989-05-01 14.942 2093500 487.6 1.119
## 162 1989-06-01 14.065 2122750 492.0 1.114
## 163 1989-07-01 14.389 2179400 497.4 1.092
## 164 1989-08-01 16.234 2207250 518.7 1.057
## 165 1989-09-01 15.361 2208800 500.8 1.029
## 166 1989-10-01 13.270 2281000 493.9 1.027
## 167 1989-11-01 13.068 2285000 490.1 0.999
## 168 1989-12-01 13.273 2285000 491.6 0.980
## 169 1990-01-01 16.010 2354750 536.4 1.042
## 170 1990-02-01 14.066 2354000 505.9 1.037
## 171 1990-03-01 14.203 2363800 503.8 1.023
## 172 1990-04-01 14.009 2380250 505.7 1.044
## 173 1990-05-01 13.743 2390000 495.3 1.061
## 174 1990-06-01 13.867 2423800 494.3 1.088
## 175 1990-07-01 13.789 2471750 496.3 1.084
## 176 1990-08-01 13.582 2509250 489.3 1.190
## 177 1990-09-01 14.025 2595400 495.1 1.294
## 178 1990-10-01 13.483 2720750 486.1 1.378
## 179 1990-11-01 12.877 2888250 482.7 1.377
## 180 1990-12-01 12.702 2968200 474.0 1.354
## 181 1991-01-01 11.596 3088000 452.7 1.247
## 182 1991-02-01 12.175 3262750 467.0 1.143
## 183 1991-03-01 12.616 3419000 495.4 1.082
## 184 1991-04-01 11.825 3483250 474.4 1.104
## 185 1991-05-01 12.270 3486750 473.5 1.156
## 186 1991-06-01 12.505 3426800 477.8 1.160
## 187 1991-07-01 12.742 3307500 485.4 1.127
## 188 1991-08-01 12.352 3291800 479.8 1.140
## 189 1991-09-01 12.890 3296250 487.9 1.143
## 190 1991-10-01 12.015 3311000 474.1 1.122
## 191 1991-11-01 12.364 3310600 477.3 1.134
## 192 1991-12-01 12.420 3349250 480.9 1.123
## 193 1992-01-01 12.367 3353250 495.6 1.073
## 194 1992-02-01 12.698 3314600 501.3 1.054
## 195 1992-03-01 12.592 3312000 491.7 1.058
## 196 1992-04-01 12.310 3343750 490.0 1.079
## 197 1992-05-01 12.860 3344800 501.9 1.136
## 198 1992-06-01 13.317 3289750 511.0 1.179
## 199 1992-07-01 12.600 3217750 507.2 1.174
## 200 1992-08-01 12.620 3250200 511.9 1.158
## 201 1992-09-01 13.114 3204250 517.1 1.158
## 202 1992-10-01 13.368 3062400 522.7 1.154
## 203 1992-11-01 12.958 2949000 514.1 1.159
## 204 1992-12-01 13.514 2823750 532.4 1.136
## 205 1993-01-01 13.193 2713000 538.1 1.117
## 206 1993-02-01 12.698 2641000 524.2 1.108
## 207 1993-03-01 13.041 2687500 521.2 1.098
## 208 1993-04-01 14.235 2775500 543.0 1.112
## 209 1993-05-01 14.212 2778400 552.0 1.129
## 210 1993-06-01 14.188 2812000 548.6 1.130
## 211 1993-07-01 14.143 2782800 558.6 1.109
## 212 1993-08-01 13.276 2803250 551.5 1.097
## 213 1993-09-01 13.676 2826000 559.8 1.085
## 214 1993-10-01 14.603 2825000 568.9 1.127
## 215 1993-11-01 14.539 2800750 574.2 1.113
## 216 1993-12-01 14.679 2762250 578.3 1.070
## 217 1994-01-01 15.035 2709600 579.6 1.043
## 218 1994-02-01 15.194 2792500 593.3 1.051
## 219 1994-03-01 14.974 2754500 593.6 1.045
## 220 1994-04-01 15.680 2736600 607.0 1.064
## 221 1994-05-01 14.249 2752750 588.9 1.080
## 222 1994-06-01 14.763 2720000 600.3 1.106
## 223 1994-07-01 14.522 2659600 602.1 1.136
## 224 1994-08-01 14.989 2647500 612.0 1.182
## 225 1994-09-01 14.911 2629250 613.8 1.177
## 226 1994-10-01 15.517 2574000 629.6 1.152
## 227 1994-11-01 15.500 2532250 635.1 1.163
## 228 1994-12-01 15.196 2528800 630.7 1.143
## 229 1995-01-01 14.363 2512500 623.6 1.129
## 230 1995-02-01 14.457 2522500 613.0 1.120
## 231 1995-03-01 14.865 2531250 627.2 1.115
## 232 1995-04-01 13.980 2519400 613.2 1.140
## 233 1995-05-01 14.418 2573000 624.8 1.200
## 234 1995-06-01 15.019 2606750 642.8 1.226
## 235 1995-07-01 14.326 2622800 631.0 1.195
## 236 1995-08-01 15.006 2607250 645.3 1.164
## 237 1995-09-01 14.959 2626800 651.3 1.148
## 238 1995-10-01 14.470 2663250 638.1 1.127
## 239 1995-11-01 15.012 2672000 650.6 1.101
## 240 1995-12-01 15.862 2612600 668.0 1.101
## 241 1996-01-01 14.449 2648750 645.8 1.129
## 242 1996-02-01 15.215 2692750 663.2 1.124
## 243 1996-03-01 15.679 2685000 670.4 1.162
## 244 1996-04-01 15.112 2633500 679.5 1.251
## 245 1996-05-01 15.624 2590500 680.1 1.323
## 246 1996-06-01 14.864 2548000 669.4 1.299
## 247 1996-07-01 14.700 2490000 672.9 1.272
## 248 1996-08-01 15.128 2494400 681.7 1.240
## 249 1996-09-01 15.184 2473000 683.6 1.234
## 250 1996-10-01 14.972 2460250 688.8 1.227
## 251 1996-11-01 15.392 2428600 692.2 1.250
## 252 1996-12-01 14.849 2487750 687.8 1.260
## 253 1997-01-01 15.332 2487750 703.3 1.261
## 254 1997-02-01 14.916 2415000 702.0 1.255
## 255 1997-03-01 15.444 2347600 711.5 1.235
## 256 1997-04-01 14.694 2324250 697.5 1.231
## 257 1997-05-01 14.717 2302800 690.2 1.226
## 258 1997-06-01 14.180 2295500 702.1 1.229
## 259 1997-07-01 15.194 2269500 715.7 1.205
## 260 1997-08-01 15.765 2272400 732.6 1.253
## 261 1997-09-01 14.711 2234750 720.0 1.277
## 262 1997-10-01 15.036 2215500 724.7 1.242
## 263 1997-11-01 15.421 2215000 740.8 1.213
## 264 1997-12-01 16.059 2235250 746.1 1.177
## 265 1998-01-01 14.400 2275200 733.0 1.131
## 266 1998-02-01 14.839 2224250 739.4 1.082
## 267 1998-03-01 15.001 2221750 740.7 1.041
## 268 1998-04-01 15.521 2182500 758.1 1.052
## 269 1998-05-01 16.649 2134600 778.3 1.092
## 270 1998-06-01 16.378 2214250 771.3 1.094
## 271 1998-07-01 14.255 2333750 769.4 1.079
## 272 1998-08-01 14.358 2237600 784.9 1.052
## 273 1998-09-01 15.862 2153000 800.9 1.033
## 274 1998-10-01 16.679 2164400 819.4 1.042
## 275 1998-11-01 15.631 2203750 817.9 1.028
## 276 1998-12-01 16.943 2220250 838.2 0.986
## 277 1999-01-01 16.094 2287200 808.4 0.972
## 278 1999-02-01 16.566 2240500 824.1 0.955
## 279 1999-03-01 16.332 2227000 827.4 0.991
## 280 1999-04-01 16.422 2237500 841.3 1.177
## 281 1999-05-01 17.059 2214400 855.4 1.178
## 282 1999-06-01 16.839 2192250 867.7 1.148
## 283 1999-07-01 17.171 2193200 865.2 1.189
## 284 1999-08-01 17.121 2180250 871.9 1.255
## 285 1999-09-01 17.116 2162000 876.4 1.280
## 286 1999-10-01 17.139 2102800 868.0 1.274
## 287 1999-11-01 17.072 2085750 871.2 1.264
## 288 1999-12-01 17.793 2097250 889.8 1.298
## 289 2000-01-01 18.114 2109800 908.6 1.301
## 290 2000-02-01 18.879 2147500 930.7 1.369
## 291 2000-03-01 17.822 2079000 923.3 1.541
## 292 2000-04-01 17.442 2015400 900.6 1.506
## 293 2000-05-01 17.467 1989500 907.0 1.498
## 294 2000-06-01 17.078 2028250 898.2 1.617
## 295 2000-07-01 16.851 2085600 897.3 1.593
## 296 2000-08-01 17.090 2117250 906.4 1.510
## 297 2000-09-01 18.245 2119600 931.3 1.582
## 298 2000-10-01 17.117 2116250 920.6 1.559
## 299 2000-11-01 16.259 2211250 913.4 1.555
## 300 2000-12-01 15.831 2306400 913.5 1.489
## 301 2001-01-01 17.251 2395750 916.5 1.472
## 302 2001-02-01 17.433 2486500 938.9 1.484
## 303 2001-03-01 16.868 2585400 925.1 1.447
## 304 2001-04-01 16.531 2697250 908.3 1.564
## 305 2001-05-01 16.503 2819250 916.0 1.729
## 306 2001-06-01 17.104 2947400 934.1 1.640
## 307 2001-07-01 16.123 3030750 925.0 1.482
## 308 2001-08-01 16.016 3120000 942.5 1.427
## 309 2001-09-01 16.056 3262200 904.1 1.531
## 310 2001-10-01 21.709 3527500 1035.1 1.362
## 311 2001-11-01 17.725 3656500 997.0 1.263
## 312 2001-12-01 16.147 3589400 955.6 1.131
## 313 2002-01-01 16.220 3550500 970.7 1.139
## 314 2002-02-01 17.005 3554250 983.2 1.130
## 315 2002-03-01 16.788 3610800 974.9 1.241
## 316 2002-04-01 17.343 3689250 1001.5 1.407
## 317 2002-05-01 15.844 3712000 962.7 1.421
## 318 2002-06-01 16.626 3635200 968.0 1.404
## 319 2002-07-01 17.829 3504250 1001.6 1.412
## 320 2002-08-01 18.105 3516800 1019.7 1.423
## 321 2002-09-01 16.326 3555000 983.9 1.422
## 322 2002-10-01 15.923 3555750 969.9 1.449
## 323 2002-11-01 16.195 3482400 978.5 1.448
## 324 2002-12-01 17.593 3482750 1009.9 1.394
## 325 2003-01-01 16.426 3414500 982.6 1.473
## 326 2003-02-01 15.814 3437000 956.4 1.641
## 327 2003-03-01 16.173 3540800 984.6 1.748
## 328 2003-04-01 16.433 3629750 1003.9 1.659
## 329 2003-05-01 16.152 3719200 1005.7 1.542
## 330 2003-06-01 16.686 3701500 1013.9 1.514
## 331 2003-07-01 16.777 3616250 1024.9 1.524
## 332 2003-08-01 17.931 3596400 1059.3 1.628
## 333 2003-09-01 16.945 3575750 1041.1 1.728
## 334 2003-10-01 16.143 3486250 1036.2 1.603
## 335 2003-11-01 17.197 3373400 1053.5 1.535
## 336 2003-12-01 16.992 3270250 1051.5 1.494
## 337 2004-01-01 16.304 3148200 1048.1 1.592
## 338 2004-02-01 16.634 3114750 1065.2 1.672
## 339 2004-03-01 16.837 3046250 1079.4 1.766
## 340 2004-04-01 16.493 3000750 1062.8 1.833
## 341 2004-05-01 17.759 2975600 1091.5 2.009
## 342 2004-06-01 15.761 2950750 1043.6 2.041
## 343 2004-07-01 16.870 2917000 1076.9 1.939
## 344 2004-08-01 16.717 2891500 1079.0 1.898
## 345 2004-09-01 17.432 2862750 1099.3 1.891
## 346 2004-10-01 17.056 2778000 1098.6 2.029
## 347 2004-11-01 16.912 2734500 1099.9 2.010
## 348 2004-12-01 17.627 2719000 1122.6 1.882
## 349 2005-01-01 16.370 2723800 1096.4 1.823
## 350 2005-02-01 16.403 2686500 1113.2 1.918
## 351 2005-03-01 16.930 2659500 1120.2 2.065
## 352 2005-04-01 17.274 2604400 1142.8 2.283
## 353 2005-05-01 16.928 2586250 1116.4 2.216
## 354 2005-06-01 17.967 2600000 1154.6 2.176
## 355 2005-07-01 20.607 2595200 1202.2 2.316
## 356 2005-08-01 16.917 2580750 1139.5 2.506
## 357 2005-09-01 16.427 2716500 1113.8 2.927
## 358 2005-10-01 14.847 2803400 1099.6 2.785
## 359 2005-11-01 16.022 2701250 1116.4 2.343
## 360 2005-12-01 16.688 2639400 1128.2 2.186
## 361 2006-01-01 17.572 2557000 1167.9 2.315
## 362 2006-02-01 16.515 2507250 1143.5 2.310
## 363 2006-03-01 16.409 2455500 1151.0 2.401
## 364 2006-04-01 16.568 2401000 1151.0 2.757
## 365 2006-05-01 16.170 2383750 1147.2 2.947
## 366 2006-06-01 16.352 2408500 1149.5 2.917
## 367 2006-07-01 17.125 2449400 1168.7 2.999
## 368 2006-08-01 15.901 2466250 1146.0 2.985
## 369 2006-09-01 16.419 2446200 1166.9 2.589
## 370 2006-10-01 16.322 2446250 1168.3 2.272
## 371 2006-11-01 16.128 2476000 1164.4 2.241
## 372 2006-12-01 16.569 2481200 1175.5 2.334
## 373 2007-01-01 16.402 2524250 1183.5 2.274
## 374 2007-02-01 16.704 2569250 1175.1 2.285
## 375 2007-03-01 16.015 2519000 1178.5 2.592
## 376 2007-04-01 16.215 2496000 1181.2 2.860
## 377 2007-05-01 16.296 2447750 1197.7 3.130
## 378 2007-06-01 15.823 2482000 1178.2 3.052
## 379 2007-07-01 15.499 2542250 1180.7 2.961
## 380 2007-08-01 16.034 2553000 1192.4 2.782
## 381 2007-09-01 16.200 2537200 1202.6 2.789
## 382 2007-10-01 16.127 2561750 1209.0 2.793
## 383 2007-11-01 16.038 2616750 1197.7 3.069
## 384 2007-12-01 15.718 2712000 1180.0 3.020
## 385 2008-01-01 15.383 2798750 1168.4 3.047
## 386 2008-02-01 15.166 2844250 1148.4 3.033
## 387 2008-03-01 14.795 2912800 1143.7 3.258
## 388 2008-04-01 14.268 2964000 1139.1 3.441
## 389 2008-05-01 14.364 3013600 1143.7 3.764
## 390 2008-06-01 14.065 3057750 1130.4 4.065
## 391 2008-07-01 12.715 3176250 1100.0 4.090
## 392 2008-08-01 13.834 3378000 1114.3 3.786
## 393 2008-09-01 12.688 3521750 1073.2 3.698
## 394 2008-10-01 10.666 3731000 1026.6 3.173
## 395 2008-11-01 10.255 4096600 1002.4 2.151
## 396 2008-12-01 10.141 4522250 995.0 1.689
## 397 2009-01-01 9.573 4861200 1023.0 1.787
## 398 2009-02-01 9.023 5297000 1006.2 1.928
## 399 2009-03-01 9.552 5765750 984.2 1.949
## 400 2009-04-01 9.197 6184500 978.8 2.056
## 401 2009-05-01 9.996 6525600 998.9 2.265
## 402 2009-06-01 9.956 6534250 1006.4 2.631
## 403 2009-07-01 11.370 6139000 1020.8 2.543
## 404 2009-08-01 14.568 6047800 1089.1 2.627
## 405 2009-09-01 9.347 5984750 995.4 2.574
## 406 2009-10-01 10.371 5765600 1003.6 2.561
## 407 2009-11-01 10.817 5460500 1017.4 2.660
## 408 2009-12-01 11.060 5141750 1021.6 2.621
## 409 2010-01-01 10.669 4839000 1006.1 2.731
## 410 2010-02-01 10.108 4793750 1005.2 2.659
## 411 2010-03-01 11.553 4731750 1052.0 2.780
## 412 2010-04-01 11.249 4728000 1046.0 2.858
## 413 2010-05-01 11.821 4674800 1041.7 2.869
## 414 2010-06-01 11.385 4583250 1044.1 2.736
## 415 2010-07-01 11.715 4528600 1047.5 2.736
## 416 2010-08-01 11.802 4461250 1053.7 2.745
## 417 2010-09-01 11.703 4467750 1056.1 2.704
## 418 2010-10-01 12.199 4363400 1079.2 2.795
## 419 2010-11-01 12.070 4195500 1077.5 2.852
## 420 2010-12-01 12.383 4100000 1078.7 2.985
## 421 2011-01-01 12.547 3918400 1085.0 3.091
## 422 2011-02-01 12.815 3849250 1083.8 3.167
## 423 2011-03-01 12.984 3765500 1095.0 3.546
## 424 2011-04-01 13.037 3770800 1090.9 3.816
## 425 2011-05-01 11.980 3773750 1081.2 3.933
## 426 2011-06-01 11.581 3741750 1076.6 3.702
## 427 2011-07-01 12.419 3721600 1085.7 3.654
## 428 2011-08-01 12.277 3716000 1085.2 3.630
## 429 2011-09-01 13.026 3742750 1101.3 3.612
## 430 2011-10-01 13.391 3701600 1114.4 3.468
## 431 2011-11-01 13.406 3652500 1107.9 3.423
## 432 2011-12-01 13.455 3573400 1114.9 3.278
## 433 2012-01-01 14.058 3451250 1130.4 3.399
## 434 2012-02-01 14.617 3381750 1145.9 3.572
## 435 2012-03-01 14.237 3336800 1138.0 3.868
## 436 2012-04-01 14.408 3323000 1137.4 3.927
## 437 2012-05-01 14.135 3328000 1133.4 3.792
## 438 2012-06-01 14.118 3324400 1129.9 3.552
## 439 2012-07-01 14.027 3305750 1134.7 3.451
## 440 2012-08-01 14.092 3306250 1138.4 3.707
## 441 2012-09-01 14.759 3314400 1151.9 3.856
## 442 2012-10-01 14.514 3276250 1141.2 3.786
## 443 2012-11-01 15.130 3326250 1169.4 3.488
## 444 2012-12-01 15.121 3190200 1180.1 3.331
## 445 2013-01-01 15.474 3132750 1192.6 3.351
## 446 2013-02-01 15.516 3058750 1193.2 3.693
## 447 2013-03-01 15.412 3037800 1180.5 3.735
## 448 2013-04-01 15.451 3041500 1182.3 3.590
## 449 2013-05-01 15.537 3012250 1187.1 3.623
## 450 2013-06-01 15.774 2992800 1186.9 3.633
## 451 2013-07-01 15.674 3020250 1190.8 3.628
## 452 2013-08-01 15.486 2934400 1187.7 3.600
## 453 2013-09-01 15.506 2895750 1188.3 3.556
## 454 2013-10-01 15.354 2928500 1191.4 3.375
## 455 2013-11-01 15.714 2851200 1201.7 3.251
## 456 2013-12-01 15.463 2858500 1190.4 3.277
## 457 2014-01-01 15.249 2876750 1173.9 3.320
## 458 2014-02-01 15.617 2848750 1204.7 3.364
## 459 2014-03-01 16.606 2779200 1231.9 3.532
## 460 2014-04-01 16.280 2721000 1230.3 3.659
## 461 2014-05-01 16.737 2667400 1238.6 3.691
## 462 2014-06-01 17.125 2588750 1249.4 3.695
## 463 2014-07-01 16.851 2533500 1248.4 3.633
## 464 2014-08-01 16.808 2504800 1258.6 3.481
## 465 2014-09-01 16.527 2447750 1259.8 3.403
## 466 2014-10-01 16.326 2423250 1263.7 3.182
## 467 2014-11-01 16.544 2408800 1272.8 2.887
## 468 2014-12-01 16.757 2380000 1272.7 2.560
## 469 2015-01-01 16.497 2349800 1272.6 2.110
## 470 2015-02-01 16.455 2340250 1276.2 2.249
## 471 2015-03-01 17.416 2326500 1297.7 2.483
## 472 2015-04-01 17.197 2281500 1304.9 2.485
## 473 2015-05-01 17.562 2260400 1311.7 2.775
## 474 2015-06-01 17.390 2273750 1306.3 2.832
## 475 2015-07-01 17.837 2244750 1315.6 2.832
## 476 2015-08-01 17.973 2254000 1319.3 2.679
## 477 2015-09-01 17.792 2243000 1315.2 2.394
## 478 2015-10-01 17.748 2207600 1307.2 2.289
## 479 2015-11-01 17.858 2208500 1321.9 2.185
## 480 2015-12-01 17.031 2203750 1322.1 2.060
## 481 2016-01-01 17.631 2204600 1321.5 1.967
## 482 2016-02-01 17.585 2188000 1341.7 1.767
## 483 2016-03-01 16.868 2166250 1326.8 1.958
## 484 2016-04-01 17.295 2155200 1334.9 2.134
## 485 2016-05-01 17.351 2175750 1338.4 2.264
## 486 2016-06-01 17.337 2159000 1356.5 2.363
## 487 2016-07-01 17.729 2141200 1364.3 2.225
## 488 2016-08-01 17.523 2148000 1360.3 2.155
## 489 2016-09-01 17.585 2113500 1370.2 2.208
## 490 2016-10-01 17.493 2075200 1373.9 2.243
## 491 2016-11-01 17.374 2048250 1356.2 2.187
## 492 2016-12-01 17.806 2056400 1387.0 2.230
## 493 2017-01-01 17.300 2036250 1385.4 2.351
## 494 2017-02-01 17.386 2018000 1386.2 2.299
## 495 2017-03-01 16.710 1996500 1383.7 2.323
## 496 2017-04-01 16.863 1974200 1394.4 2.418
## 497 2017-05-01 16.851 1944000 1394.8 2.386
## 498 2017-06-01 16.796 1962500 1406.8 2.337
## 499 2017-07-01 16.771 1961000 1411.4 2.281
## 500 2017-08-01 16.590 1953500 1402.0 2.374
## 501 2017-09-01 17.951 1951000 1434.3 2.630
## 502 2017-10-01 17.768 1922750 1441.4 2.484
## 503 2017-11-01 17.416 1927500 1452.5 2.548
## 504 2017-12-01 17.235 1894600 1457.7 2.459
## 505 2018-01-01 17.130 1908250 1455.5 2.539
## 506 2018-02-01 17.080 1860250 1453.2 2.575
## 507 2018-03-01 17.220 1830400 1455.7 2.572
## 508 2018-04-01 17.312 1813250 1471.6 2.737
## 509 2018-05-01 17.309 1746750 1481.0 2.907
## 510 2018-06-01 17.215 1734000 1477.4 2.914
## 511 2018-07-01 16.890 1749500 1481.0 2.873
## 512 2018-08-01 16.857 1723750 1489.6 2.862
## 513 2018-09-01 17.316 1685400 1485.0 2.873
## 514 2018-10-01 17.478 1657500 1489.6 2.887
## 515 2018-11-01 17.379 1681250 1509.3 2.671
## 516 2018-12-01 17.377 1695200 1458.0 2.414
## 517 2019-01-01 16.711 1716750 1483.4 2.289
## 518 2019-02-01 16.519 1729250 1463.8 2.353
## 519 2019-03-01 17.260 1724000 1508.9 2.564
## 520 2019-04-01 16.483 1673000 1507.8 2.835
## 521 2019-05-01 17.385 1682750 1529.0 2.901
## 522 2019-06-01 17.178 1697000 1537.1 2.752
## 523 2019-07-01 16.876 1691750 1541.8 2.776
## 524 2019-08-01 16.974 1694200 1545.6 2.655
## 525 2019-09-01 17.148 1678000 1561.7 2.630
## 526 2019-10-01 16.520 1693750 1541.7 2.673
## 527 2019-11-01 16.985 1692200 1553.1 2.620
## 528 2019-12-01 16.647 1736000 1548.1 2.587
## 529 2020-01-01 16.914 1737750 1557.7 2.567
## 530 2020-02-01 16.737 1703800 1550.0 2.465
## 531 2020-03-01 11.372 3497750 1124.0 2.267
set.seed(420)
#train and test data for car sales
split3=caTools::sample.split(car_sales$Sales,SplitRatio = 0.8)
training_set3=subset(car_sales,split3==TRUE)
test_set3=subset(car_sales,split3==FALSE)
#Fitting regression model to the training set (Unemployment, Consumption, and GasPrices)
regressor3=lm(formula=car_sales$Sales[0:531]~Unemployment$Unemployed[109:639] + Consumption$Personal_Consumption[205:735] + GasPrices$Gas_Price[0:531],
data=training_set3)
#Prediction values
y_pred3=predict(regressor3,newdata=test_set3)
## Warning: 'newdata' had 107 rows but variables found have 531 rows
#plot new prediction (best fit)
new_date3 = car_sales$Date[0:531]
car_pred3 = plot_ly(car_sales, x = new_date3, y = y_pred3, type= 'scatter', mode='lines', color='red')
car_pred3
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
#subplot show advancement in our model (Blue is OG data, Orange are models to fit, bottom graph being most advanced)
subplot(car_plot, car_pred1, car_pred2, car_pred3, nrows = 4, margin = 0.05, shareX = TRUE)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
#residual plot
plot(residuals(regressor3))
#plot forecast with our prediction model 3, 36 month term
data2 = data.frame(Date = new_date3,
Prediction = y_pred3)
ts.data = ts(data2, frequency=12)
fit <- auto.arima(ts.data[,2], D=1)
fore <- forecast(fit, h=36)
plot(fore)
#plot forecast with our prediction model 3, 72 month term
data3 = data.frame(Date = new_date3,
Prediction = y_pred3)
ts.data1 = ts(data3, frequency=12)
fit1 <- auto.arima(ts.data1[,2], D=1)
fore1 <- forecast(fit, h=72)
plot(fore1)
#Forecasted values (3yr)
fore
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Apr 45 13.17735 12.85911 13.49559 12.690646 13.66406
## May 45 12.69182 12.15186 13.23179 11.866015 13.51763
## Jun 45 12.26681 11.55478 12.97885 11.177857 13.35577
## Jul 45 12.10190 11.23457 12.96923 10.775434 13.42837
## Aug 45 12.07533 11.06862 13.08204 10.535700 13.61496
## Sep 45 12.07100 10.94157 13.20042 10.343692 13.79830
## Oct 45 12.22256 10.98413 13.46098 10.328553 14.11656
## Nov 45 12.42809 11.09205 13.76413 10.384788 14.47139
## Dec 45 12.63111 11.20723 14.05499 10.453479 14.80875
## Jan 46 12.73769 11.23435 14.24104 10.438523 15.03687
## Feb 46 12.80448 11.22885 14.38011 10.394757 15.21420
## Mar 46 12.60391 10.96222 14.24560 10.093158 15.11466
## Apr 46 12.51248 10.80663 14.21834 9.903603 15.12137
## May 46 12.49557 10.72939 14.26175 9.794424 15.19671
## Jun 46 12.58190 10.75972 14.40409 9.795109 15.36870
## Jul 46 12.74447 10.87010 14.61885 9.877860 15.61108
## Aug 46 12.86950 10.94649 14.79251 9.928508 15.81049
## Sep 46 12.95284 10.98452 14.92115 9.942562 15.96311
## Oct 46 13.14922 11.13866 15.15978 10.074330 16.22411
## Nov 46 13.38980 11.33978 15.43982 10.254565 16.52503
## Dec 46 13.58866 11.50175 15.67557 10.397005 16.78031
## Jan 47 13.70955 11.58811 15.83100 10.465082 16.95402
## Feb 47 13.75095 11.59714 15.90476 10.456978 17.04492
## Mar 47 13.57966 11.39548 15.76384 10.239246 16.92008
## Apr 47 13.47476 11.26033 15.68920 10.088073 16.86145
## May 47 13.45449 11.21095 15.69802 10.023300 16.88567
## Jun 47 13.54347 11.27239 15.81455 10.070149 17.01679
## Jul 47 13.68372 11.38650 15.98094 10.170432 17.19701
## Aug 47 13.80274 11.48079 16.12469 10.251619 17.35386
## Sep 47 13.88155 11.53623 16.22687 10.294696 17.46841
## Oct 47 14.03602 11.66863 16.40340 10.415414 17.65662
## Nov 47 14.25636 11.86813 16.64458 10.603879 17.90883
## Dec 47 14.42385 12.01594 16.83176 10.741273 18.10643
## Jan 48 14.53136 12.10485 16.95787 10.820340 18.24238
## Feb 48 14.57249 12.12841 17.01657 10.834589 18.31038
## Mar 48 14.01340 11.55270 16.47409 10.250091 17.77670
#forecasted values (6yr)
fore1
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Apr 45 13.17735 12.85911 13.49559 12.690646 13.66406
## May 45 12.69182 12.15186 13.23179 11.866015 13.51763
## Jun 45 12.26681 11.55478 12.97885 11.177857 13.35577
## Jul 45 12.10190 11.23457 12.96923 10.775434 13.42837
## Aug 45 12.07533 11.06862 13.08204 10.535700 13.61496
## Sep 45 12.07100 10.94157 13.20042 10.343692 13.79830
## Oct 45 12.22256 10.98413 13.46098 10.328553 14.11656
## Nov 45 12.42809 11.09205 13.76413 10.384788 14.47139
## Dec 45 12.63111 11.20723 14.05499 10.453479 14.80875
## Jan 46 12.73769 11.23435 14.24104 10.438523 15.03687
## Feb 46 12.80448 11.22885 14.38011 10.394757 15.21420
## Mar 46 12.60391 10.96222 14.24560 10.093158 15.11466
## Apr 46 12.51248 10.80663 14.21834 9.903603 15.12137
## May 46 12.49557 10.72939 14.26175 9.794424 15.19671
## Jun 46 12.58190 10.75972 14.40409 9.795109 15.36870
## Jul 46 12.74447 10.87010 14.61885 9.877860 15.61108
## Aug 46 12.86950 10.94649 14.79251 9.928508 15.81049
## Sep 46 12.95284 10.98452 14.92115 9.942562 15.96311
## Oct 46 13.14922 11.13866 15.15978 10.074330 16.22411
## Nov 46 13.38980 11.33978 15.43982 10.254565 16.52503
## Dec 46 13.58866 11.50175 15.67557 10.397005 16.78031
## Jan 47 13.70955 11.58811 15.83100 10.465082 16.95402
## Feb 47 13.75095 11.59714 15.90476 10.456978 17.04492
## Mar 47 13.57966 11.39548 15.76384 10.239246 16.92008
## Apr 47 13.47476 11.26033 15.68920 10.088073 16.86145
## May 47 13.45449 11.21095 15.69802 10.023300 16.88567
## Jun 47 13.54347 11.27239 15.81455 10.070149 17.01679
## Jul 47 13.68372 11.38650 15.98094 10.170432 17.19701
## Aug 47 13.80274 11.48079 16.12469 10.251619 17.35386
## Sep 47 13.88155 11.53623 16.22687 10.294696 17.46841
## Oct 47 14.03602 11.66863 16.40340 10.415414 17.65662
## Nov 47 14.25636 11.86813 16.64458 10.603879 17.90883
## Dec 47 14.42385 12.01594 16.83176 10.741273 18.10643
## Jan 48 14.53136 12.10485 16.95787 10.820340 18.24238
## Feb 48 14.57249 12.12841 17.01657 10.834589 18.31038
## Mar 48 14.01340 11.55270 16.47409 10.250091 17.77670
## Apr 48 13.75379 11.27263 16.23494 9.959190 17.54839
## May 48 13.68256 11.18010 16.18502 9.855379 17.50974
## Jun 48 13.71623 11.19305 16.23941 9.857361 17.57510
## Jul 48 13.82079 11.27738 16.36419 9.930989 17.71058
## Aug 48 13.91930 11.35637 16.48222 9.999639 17.83895
## Sep 48 13.98362 11.40205 16.56519 10.035448 17.93179
## Oct 48 14.13111 11.53179 16.73043 10.155798 18.10643
## Nov 48 14.34362 11.72744 16.95979 10.342514 18.34472
## Dec 48 14.50810 11.87592 17.14027 10.482536 18.53366
## Jan 49 14.60940 11.96207 17.25674 10.560659 18.65815
## Feb 49 14.64702 11.98533 17.30872 10.576310 18.71774
## Mar 49 14.11406 11.43875 16.78937 10.022528 18.20559
## Apr 49 13.86390 11.17181 16.55599 9.746704 17.98110
## May 49 13.79205 11.08245 16.50165 9.648076 17.93603
## Jun 49 13.82506 11.09840 16.55172 9.654989 17.99513
## Jul 49 13.92967 11.18632 16.67302 9.734083 18.12526
## Aug 49 14.02548 11.26599 16.78497 9.805210 18.24575
## Sep 49 14.08655 11.31163 16.86148 9.842672 18.33044
## Oct 49 14.23374 11.44410 17.02339 9.967353 18.50014
## Nov 49 14.44411 11.64046 17.24776 10.156302 18.73192
## Dec 49 14.60718 11.79023 17.42413 10.299031 18.91533
## Jan 50 14.70579 11.87622 17.53537 10.378333 19.03325
## Feb 50 14.73936 11.89781 17.58091 10.393579 19.08514
## Mar 50 14.23609 11.38317 17.08900 9.872931 18.59924
## Apr 50 13.99539 11.12849 16.86230 9.610843 18.37994
## May 50 13.92429 11.04279 16.80579 9.517411 18.33117
## Jun 50 13.95856 11.06281 16.85430 9.529895 18.38722
## Jul 50 14.06262 11.15293 16.97231 9.612637 18.51260
## Aug 50 14.15680 11.23362 17.07999 9.686173 18.62743
## Sep 50 14.21583 11.27971 17.15194 9.725424 18.70623
## Oct 50 14.36009 11.41164 17.30854 9.850818 18.86936
## Nov 50 14.56786 11.60766 17.52807 10.040617 19.09510
## Dec 50 14.72791 11.75653 17.69929 10.183576 19.27224
## Jan 51 14.82399 11.84200 17.80598 10.263425 19.38455
## Feb 51 14.85498 11.86291 17.84705 10.278998 19.43096
## Mar 51 14.34342 11.34178 17.34506 9.752806 18.93403